The power of eDNA sampling to investigate the impact of Australian mega-fires on platypus occupancy
Bibliographic record
Abstract
Fire plays an important role in many ecosystems, but megafires are increasing the area burnt in forested regions globally. The 2019–2020 megafires in south-eastern Australia were unprecedented with respect to area burnt and the spatial extent of high-severity fire. Yet, there is limited knowledge regarding the impact of these megafires on biodiversity, especially aquatic fauna. Here we investigate the impact of the 2019–2020 megafires on the distribution of a semi-aquatic monotreme thought to be in decline: the platypus, Ornithorhynchus anatinus. We leveraged extensive pre-fire environmental DNA (eDNA) sampling and coupled this with additional sampling at two timepoints post-fire, to conduct a Before-After-Control-Impact (BACI) study. We used site occupancy-detection modelling to estimate platypus occupancy across fire affected and non-fire affected sites over time as well as the interaction between occupancy, high rainfall, and fire severity. We detected a negative effect of fire presence on platypus occupancy in both post-fire time periods. Platypus occupancy was also predicted to be lower at sites that experienced high rainfall post-fire and were situated within watersheds that had a large proportion burnt at high-severity. With area burnt and the extent of high-severity fire increasing globally, and predictions of more extreme rainfall events in south-eastern Australia in the future, the impact of fire on aquatic fauna requires greater consideration in post-fire assessments and biodiversity management more generally. The use of eDNA sampling and detection methods in a BACI framework provides a promising means to fill these knowledge gaps but does require pre-emptive sampling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".